Evidence map›Paper›PMID 37895577›Full record

ArticleEntropy (Basel, Switzerland)2023

Synchronization of Complex Dynamical Networks with Stochastic Links Dynamics.

Juanxia Zhao, Yinhe Wang, Peitao Gao, Shengping Li, Yi Peng

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Juanxia ZhaoSchool of Automation, Guangdong University of Technology, Guangzhou 510006, China.ORCID 0000-0002-0110-8959
Yinhe WangSchool of Automation, Guangdong University of Technology, Guangzhou 510006, China.
Peitao GaoSchool of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
Shengping LiMOE Key Laboratory of Intelligent Manufacturing, Shantou University, Shantou 515063, China.
Yi PengSchool of Automation, Guangdong University of Technology, Guangzhou 510006, China.

Funding

Key Laboratory of Intelligent Manufacturing Technology (Shan-tou University), Ministry of Education of China 202109242
6 · The paper itself

Abstract

The mean square synchronization problem of the complex dynamical network (CDN) with the stochastic link dynamics is investigated. In contrast to previous literature, the CDN considered in this paper can be viewed as consisting of two subsystems coupled to each other. One subsystem consists of all nodes, referred to as the nodes subsystem, and the other consists of all links, referred to as the network topology subsystem, where the weighted values can quantitatively reflect changes in the network's topology. Based on the above understanding of CDN, two vector stochastic differential equations with Brownian motion are used to model the dynamic behaviors of nodes and links, respectively. The control strategy incorporates not only the controller in the nodes but also the coupling term in the links, through which the CDN is synchronized in the mean-square sense. Meanwhile, the dynamic stochastic signal is proposed in this paper, which is regarded as the auxiliary reference tracking target of links, such that the links can track the reference target asymptotically when synchronization occurs in nodes. This implies that the eventual topological structure of CDN is stochastic. Finally, a comparison simulation example confirms the superiority of the control strategy in this paper.

Indexed as

control strategydynamics of linksmean square synchronizationstochastic complex dynamical network

Identifiers

PMID37895577
PMCPMC10606096

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.